You will be responsible for developing and implementing machine learning solutions that power our AI-native platform. This role requires expertise in custom model development, transformer architectures, and production deployment of ML systems. Responsibilities • Design and develop custom machine learning models for specific business use cases. • Implement and optimize transformer architectures for natural language processing tasks. • Develop and maintain ML pipelines for data preprocessing, model training, and inference. • Work with large language models and implement fine-tuning strategies. • Implement retrieval-augmented generation (RAG) systems and optimize their performance. • Collaborate with engineering teams to deploy ML models in production environments. • Monitor and maintain model performance in production, implementing retraining strategies as needed. • Conduct research on emerging ML techniques and evaluate their applicability to our platform. • Mentor junior ML engineers and contribute to best practices within the team. Required Skills and Qualifications • Master's degree or PhD in Computer Science, Machine Learning, or a related field, or equivalent practical experience. • 5+ years of experience in machine learning development and deployment. • Strong programming skills in Python and experience with ML frameworks (PyTorch, TensorFlow, or similar). • Deep understanding of transformer architectures and their applications in NLP. • Experience with large language models and fine-tuning techniques. • Proficiency in implementing and optimizing RAG systems. • Experience with ML model deployment and production monitoring. • Strong understanding of data preprocessing, feature engineering, and model evaluation techniques. • Experience with cloud platforms and ML infrastructure (AWS SageMaker, Google Vertex AI, or similar). • Excellent problem-solving skills and attention to detail. • Strong communication and interpersonal skills. Preferred Qualifications • Experience with MLOps and ML pipeline orchestration tools. • Knowledge of distributed training and model optimization techniques. • Experience with vector databases and similarity search algorithms. • Familiarity with reinforcement learning and multi-agent systems.